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Robust optimisation algorithm for the measurement matrix in compressed sensing
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Robust optimisation algorithm for the measurement matrix in compressed sensing
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Journal Article
Robust optimisation algorithm for the measurement matrix in compressed sensing
2018
Overview
The measurement matrix which plays an important role in compressed sensing has got a lot of attention. However, the existing measurement matrices ignore the energy concentration characteristic of the natural images in the sparse domain, which can help to improve the sensing efficiency and the construction efficiency. Here, the authors propose a simple but efficient measurement matrix based on the Hadamard matrix, named Hadamard-diagonal matrix (HDM). In HDM, the energy conservation in the sparse domain is maximised. In addition, considering the reconstruction performance can be further improved by decreasing the mutual coherence of the measurement matrix, an effective optimisation strategy is adopted in order to reducing the mutual coherence for better reconstruction quality. The authors conduct several experiments to evaluate the performance of HDM and the effectiveness of optimisation algorithm. The experimental results show that HDM performs better than other popular measurement matrices, and the optimisation algorithm can improve the performance of not only the HDM but also the other popular measurement matrices.
Publisher
The Institution of Engineering and Technology,John Wiley & Sons, Inc,Wiley
Subject
(B0260) Optimisation techniques
/ (B0290H) Linear algebra (numerical analysis)
/ (B6135) Optical, image and video signal processing
/ (C1180) Optimisation techniques
/ (C4140) Linear algebra (numerical analysis)
/ (C5260B) Computer vision and image processing techniques
/ effective optimisation strategy
/ energy concentration characteristic
/ HDM
/ popular measurement matrices
/ robust optimisation algorithm
/ Sparsity
/ Special Issue: Selected Papers from The 4th Asian Conference on Pattern Recognition (ACPR 2017)
